Contentious Responses to the Crises in Spain : Emphasis Frames and Public Support for Protest on Twitter and the Press.

This research analyzes how different types of frames adopted by news organizations and social media affected the support for protests against austerity measures in Spain. We pay special attention to the individual understandings of the crises based on materialistic grievances and nonmaterialistic accounts of the economic crises. We identified frames using a supervised approach and a data set of 2 million tweets gathered during the major demonstrations of the Indignados Movement between 2011 and 2013. Our second data set included the news-related content published by the top Spanish newspapers, in terms of circulation, during those demonstrations. We found that support for antiausterity protests was conditioned by understandings of the crisis. Frames addressing the political system were negatively related to the support for protests against austerity measures, as compared with those referring to the crisis as an economic matter. In addition, more challenging and controversial frames generated lower acceptance of the protests among the population than those that primed social problems

Multi-Task Attentive Residual Networks for Argument Mining

We explore the use of residual networks and neural attention for argument mining and in particular link prediction. The method we propose makes no assumptions on document or argument structure. We propose a residual architecture that exploits attention, multi-task learning, and makes use of ensemble. We evaluate it on a challenging data set consisting of user-generated comments, as well as on two other datasets consisting of scientific publications. On the user-generated content dataset, our model outperforms state-of-the-art methods that rely on domain knowledge. On the scientific literature datasets it achieves results comparable to those yielded by BERT-based approaches but with a much smaller model size.Comment: 12 pages, 2 figures, submitted to IEEE Transactions on Neural Networks and Learning System

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot

Computational Argumentation Approaches to Improve Sensemaking and Evidence-based Reasoning in Online Deliberation Systems

Deliberation is the process through which communities identify potential solutions for a problem and select the solution that most effectively meets their diverse requirements through dialogic communication. Online deliberation is implemented nowadays with means of social media and online discussion platforms; however, these media present significant challenges and issues that can be traced to inadequate support for Sensemaking processes and poor endorsement of the quality characteristics of deliberation. This thesis investigates integrating computational argumentation methods in online deliberation platforms as an effective way to improve participants' perception of the quality of the deliberation process, their way of making sense of the overall process and producing healthier social dynamics. For that, two computational artefacts are proposed: (i) a Synoptical summariser of long discussions and (ii) a Scientific Argument Recommender System (SciArgRecSys). The two artefacts are designed and developed with state-of-the-art methods (with the use of Large Language Models - LLMs) and evaluated intrinsically and extrinsically when deployed in a real live platform (BCause). Through extensive evaluation, the positive effect of both artefacts is illustrated in human Sensemaking and essential quality characteristics of deliberation such as reciprocal Engagement, Mutual Understanding, and Social dynamics. In addition, it has been demonstrated that these interventions effectively reduce polarisation, the formation of sub-communities while significantly enhancing the quality of the discussion by making it more coherent and diverse

Grammar and Corpora 2016

In recent years, the availability of large annotated corpora, together with a new interest in the empirical foundation and validation of linguistic theory and description, has sparked a surge of novel work using corpus methods to study the grammar of natural languages. This volume presents recent developments and advances, firstly, in corpus-oriented grammar research with a special focus on Germanic, Slavic, and Romance languages and, secondly, in corpus linguistic methodology as well as the application of corpus methods to grammar-related fields. The volume results from the sixth international conference Grammar and Corpora (GaC 2016), which took place at the Institute for the German Language (IDS) in Mannheim, Germany, in November 2016

Representing and Redefining Specialised Knowledge: Medical Discourse

This volume brings together five selected papers on medical discourse which show how specialised medical corpora provide a framework that helps those engaging with medical discourse to determine how the everyday and the specialised combine to shape the discourse of medical professionals and non-medical communities in relation to both long and short-term factors. The papers contribute, in an exemplary way, to illustrating the shifting boundaries in today’s society between the two major poles making up the medical discourse cline: healthcare discourse at the one end, which records the demand for personalised therapies and individual medical services; and clinical discourse the other, which documents research into society’s collective medical needs